The myth of work-study balance: student strategies and the limits of resilience
Bibliographic record
Abstract
This article explores how students negotiate the competing demands of work and study in neoliberal higher education systems, a context where austerity policies and market logic have intensified pressures on both institutions and learners. Drawing on 62 interviews with post-secondary students in the Greater Toronto Area, I examine their adaptive strategies, such as selective employment, which both alleviate and expose systemic shortcomings. When such strategies collapse under unsustainable pressures, like rigid class schedules that conflict with work shifts or tuition hikes outpacing wages, students are forced to further adapt, reducing course loads, delaying graduation, or stretching already tight budgets. Dominant narratives frame work-study balance as a matter of personal time management, obscuring how its deeper structural, cultural, and social dimensions render balance unattainable. By tracing how students’ efforts falter despite their resourcefulness, the analysis challenges neoliberal assumptions that individualize responsibility while obscuring institutional accountability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.069 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".